# Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning?

arXiv: https://arxiv.org/abs/2401.17571 · HTML: https://mlsanity.com/paper/2401.17571/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images](https://mlsanity.com/paper/2103.02772/) (2021)
- [DISA: DIfferentiable Similarity Approximation for Universal Multimodal Registration](https://mlsanity.com/paper/2307.09931/) (2023)
- [Semantic similarity metrics for learned image registration](https://mlsanity.com/paper/2104.10051/) (2021)
- [New starting point registration method for tagged MRI tongue motion estimation](https://mlsanity.com/paper/2302.04330/) (2023)
- [DRIMET: Deep Registration for 3D Incompressible Motion Estimation in Tagged-MRI with Application to the Tongue](https://mlsanity.com/paper/2301.07234/) (2023)
- [MomentaMorph: Unsupervised Spatial-Temporal Registration with Momenta, Shooting, and Correction](https://mlsanity.com/paper/2308.02949/) (2023)

## Built on by (most cited first, 2 of 2)

- [Brightness-Invariant Tracking Estimation in Tagged MRI](https://mlsanity.com/paper/2505.18365/) (2025)
- [Solving a Nonlinear Blind Inverse Problem for Tagged MRI with Physics and Deep Generative Priors](https://mlsanity.com/paper/2603.00882/) (2026)

## Abstract (arXiv)

Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for quantifying tissue motion and strain during deformation. However, a phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-processing. The first contribution of this study is to model tag fading by considering the interplay between T1 relaxation and the repeated application of radio frequency (RF) pulses during serial imaging sequences. This is a factor that has been overlooked in prior research on tMRI post-processing. Further, we have observed an emerging trend of utilizing raw tagged MRI within a deep learning-based (DL) registration framework for motion estimation. In this work, we evaluate and analyze the impact of commonly used image similarity objectives in training DL registrations on raw tMRI. This is then compared with the Harmonic Phase-based approach, a traditional approach which is claimed to be robust to tag fading. Our findings, derived from both simulated images and an actual phantom scan, reveal the limitations of various similarity losses in raw tMRI and emphasize caution in registration tasks where image intensity changes over time.